I Built SlothFlow AI: A Local-First AI Assistant That Turns Brain Dumps Into Action Plans 🦥

Описание и применение

SlothFlow AI преобразует неструктурированные заметки в управляемые задачи, используя локальный LLM или правила, и хранит их локально, помогая пользователям сосредоточиться на выполнении, а не на планировании.

Задача

Users struggle to decide where to start when overwhelmed with tasks and brain dumps.

Результат

SlothFlow AI reduces manual organization effort by converting brain dumps into structured task plans locally, enabling users to focus on execution rather than planning.

Рабочий процесс

• 1. User writes a brain dump in plain language.
• 2. SlothFlow AI processes the input using a local LLM (Ollama) or a rules‑based fallback.
• 3. AI outputs a draft task list with priorities and time estimates.
• 4. User reviews and saves selected tasks to the workspace.
• 5. Tasks are tracked, completed, or deleted in the local SQLite database.
• 6. Daily Reset view summarizes unfinished work and estimated time.
• 7. User can add new tasks manually or adjust existing ones.

Доказательства

I wanted to build something that could help with that problem, so for the Hacktoberfest DEV Challenge Week 3, I built SlothFlow AI — a local-first productivity assistant designed to turn overwhelming brain dumps into manageable action plans.

Источник

When a compatible local model is available, SlothFlow uses it to generate a more contextual task plan. If Ollama isn't available, a built-in rules-based fallback can still produce a basic checklist.

Источник

The project uses a lightweight stack: Python — application logic, FastAPI — REST API and backend, Ollama — optional local LLM inference, SQLite — local task persistence, HTML, CSS and JavaScript — responsive frontend, Pytest — automated testing.

Источник